Glaucoma is a leading cause of blindness and is characterized by the progressive loss of axons in the optic nerve. According to the World Health Organisation's Global Data Bank on Blindness, glaucoma accounts for 5.1 million out of an estimated 38 million blind people, or about 13.4%, worldwide. As the world's population rapidly ages, glaucoma morbidity will rise, resulting in increased health care costs and economic burden. There is no cure for glaucoma, as damage to the axons is permanent and as yet is irrecoverable. However, with early detection, the advancement of glaucomatous optical neuropathy can be significantly slowed or even halted. Mass screening is thus critical to prevent the further development of glaucomatous damage. However, no ideal community-based screening test has been found. Such a test would be beneficial for diagnostic as well as therapeutic purposes. Glaucomatous optic nerve damage precedes clinical identifiable visual loss, so early detection of glaucoma optic nerve damage through medical imaging technology would help ophthalmologists to identify, monitor and discover new ways to manage patients and slow the progression of the condition. This test should ideally be clinician-independent, rapid, non-invasive and have a very high specificity.
A classic feature of glaucoma is the specific abnormal appearance of the optic nerve head: cupping or excavation of the optic disc, with loss of the neuroretinal rim, typically seen as an enlargement of the optic disc cup to disc ratio (CDR). FIG. 1 shows enlargement of the optic cup and increase in the CDR over a 10 year period. FIG. 1(a) shows a normal eye, and FIG. 1(b) the same eye after 10 years. The central bright area of FIG. 1(a) is the optic cup, while the well-defined ellipse is the disc. As shown in FIG. 1(b), the optic cup has gradually enlarged to fill much of the disc. The CDR is regarded as an important indicator for detecting the presence of glaucoma in a patient, as well as the extent of glaucomatous optical neuropathy. In current clinical practice, the CDR is measured manually by an ophthalmologist and is subjective due to differences in the intra-observer experiences and training. The reliance on manual efforts restricts the use of the CDR for deployment in mass screening.
There are currently 3 main medical imaging modalities for glaucoma diagnosis: retinal fundus photography, optical coherence tomography (OCT) and Heidelberg Retinal Tomography (HRT).
Retinal fundus photography makes use of a fundus camera to capture images of the retinal fundus, including the optic disc and is currently the standard employed approach for observing and diagnosing ocular maladies. The camera is based on the principle of monocular indirect ophthalmoscopy.
OCT is an interferometric, non-invasive optical tomographic imaging technology capable of achieving sub-micrometer resolution due to its cross-sectional imaging capabilities. HRT is a confocal laser scanning system designed for acquisition and analysis of 3D images of the posterior segment. HRT enables the quantitative assessment of the topography of ocular structure and the precise follow-up of the topographic changes. However, OCT and HRT systems suffer numerous disadvantages. Firstly, they are expensive and require a greater expertise to operate when compared with the retinal photography-based systems. The effectiveness of OCT for glaucoma diagnosis is restricted by the technology's limited depth penetration and lack of true color data. The Image quality obtained via OCT is also dependent on operator technique and can be degraded in the presence of media opacity. Some parameters obtained with OCT may be affected by structural changes around the optic disc head. Change analysis software for glaucoma applications is not fully developed, and there is a scarcity of age, gender, and race-specific normative data upon which to compare eyes with retinal disease and glaucoma. For HRT, although the test only requires a few seconds to perform, the results are extremely sensitive to patient movements, including eye and head movements, and blinks, disrupting the laser's path, and impairing the quality of the obtained image.
In attempting to make use of retinal fundus images, researchers have focused their efforts on the automatic segmentation of the optic disc. However, there have been much less research work towards the detection of the optic cup due to the cup's interweavement with blood vessel and surrounding tissues.
In a first prior art reference [1], optic disc shape detection is done using a “modified” active shape model (ASM). It reaches a disc boundary detection rate of 94% on 100 datasets collected by the authors. No cup or CDR calculation were done in this paper. ASM is a searching procedure in which a “shape model” (a smooth shape in the image space defined by a plurality of parameters) is fitted to data, to produce a model termed a point distribution model (PDM). Modified ASM (as described below) improves conventional active shape models by adding a self-adjusting weight, and excluding the outlying points from the images. Specifically, a set of n “landmark points” is defined on the image. These points are transformed into “shape space” by a transform based on the numerical parameters of the model. For each landmark point, a corresponding “matching point” is found using a first derivative of the image intensity (or, in the case of a part of the disc boundary which is not well-defined because of blood vessels in the image, based on nearby matching points or landmark points), and the landmark points are then updated also using an energy function. This process is repeated iteratively. Writing the set of “matching points” in a given iteration as Y={Yi} for i=1, . . . n, a corresponding set of updated landscape points {Xi} is produced by minimizing the energy function:
                              E          τ                =                              ∑                          i              =              1                        n                    ⁢                                                    (                                                      Y                    i                                    -                                      X                    i                                                  )                            T                        ⁢                                          W                i                            ⁡                              (                                                      Y                    i                                    -                                      X                    i                                                  )                                                                        (                  1.2          ⁢          .1                )            
It is the set of parameters Wi which cause the modification of standard ASM. The function (1.2.1) is minimized twice with respect to the set of values Xi in each iteration. The first time, the set of parameters Wi is initialized to
      W    i    =      {                            1                                                    Y              i                        ⁢                                                  ⁢            is            ⁢                                                  ⁢            detected            ⁢                                                  ⁢            directly                                                0.7                                                    Y              i                        ⁢                                                  ⁢            is            ⁢                                                  ⁢            estimated            ⁢                                                  ⁢            by            ⁢                                                  ⁢            nearby            ⁢                                                  ⁢            matching            ⁢                                                  ⁢            points                                                0                                                    Y              i                        ⁢                                                  ⁢            is            ⁢                                                  ⁢            updated            ⁢                                                  ⁢            by            ⁢                                                  ⁢                          X              i                                          
Setting Wi to zero for some values of i eliminates the effect of points for which neither Yi nor nearby matching points can be detected. The second time, Wi is adjusted to be the following:
      W    i    =      {                            1                                                    E              i                        <            5                                                            5            /                          E              i                                                            5            ≤                          E              i                        ≤            15                                                            1            /                          E              i                                                                          E              i                        >            15                              where Ei is the Euclidean distance between Xi and Yi, and the function 1.2.1 is minimized again.
A second prior art reference [2] is based on the Chan-Vese (C-V) model for segmentation introduced by Tony Chan and Luminita Vese. This achieved Optic disc detection of 94% using a database containing 50 color fundus images, including 20 low contrast ones. This approach is formulated by defining a ‘fitting energy’ function E as the integral over the image space of a Lipschitz function φ(x, y) of coordinates (x, y) in the image space, and minimizing the fitting energy with respect to φ(x, y) subject to an elliptic shape restraint. No Cup and CDR calculation were done in this paper.
A third prior art reference [3] describes using discriminatory analysis to decide thresholds, and subsequently utilizes these to segment the cup and disc. Although the Cup-to-Disc Ratio (CDR) was measured in this paper, no clear results are given and the image sets used for testing are not clearly described.
A fourth prior art reference [4], describes disc and cup segmentation and CDR calculation. The optic disc center is found by applying the Circular Hough Transformation, and subsequently the disc boundary detection is done through an active shape model (ASM) by defining 72 points around the disc first. Finally, a first energy function is used to deform the contour to the best shape. The energy function depends on five energy terms weighted by respective parameters. Cup detection employs an initial estimate based on the disc contour detected earlier, and then deforms this using a second energy function. This method achieves the following mean and standard deviation of the percentage error rates, as shown in Table I:
TABLE IPerformance statistics of prior art reference [4]MetricValueMean percentage error in finding−4.334012789CDRStandard deviation percentage12.59088846error